---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: deberta-v3-large-test-231
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# deberta-v3-large-test-231

This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0695
- Precision: 0.9900
- Recall: 0.9900
- F1: 0.9900
- Accuracy: 0.9900

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0663        | 1.0   | 1994 | 0.0489          | 0.9878    | 0.9878 | 0.9878 | 0.9878   |
| 0.0383        | 2.0   | 3988 | 0.0584          | 0.9850    | 0.9850 | 0.9850 | 0.9850   |
| 0.0138        | 3.0   | 5982 | 0.0783          | 0.9870    | 0.9870 | 0.9870 | 0.9870   |
| 0.0026        | 4.0   | 7976 | 0.0691          | 0.9878    | 0.9878 | 0.9878 | 0.9878   |
| 0.0016        | 5.0   | 9970 | 0.0695          | 0.9900    | 0.9900 | 0.9900 | 0.9900   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1